{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2\n",
    "import os\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# For displaying multiple outputs\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "image_dir = '/home/gaurav/datasets/agri/Tomato_Classifier/MultiClassData/'\n",
    "\n",
    "healthy_images_dir = image_dir + 'Tomato___healthy/'\n",
    "bacterial_spot_images_dir = image_dir + 'Tomato___Bacterial_spot/'\n",
    "early_blight_images_dir = image_dir + 'Tomato___Early_blight/'\n",
    "late_blight_images_dir = image_dir + 'Tomato___Late_blight/'\n",
    "leaf_mold_images_dir = image_dir + 'Tomato___Leaf_Mold/'\n",
    "septoria_leaf_spot_images_dir = image_dir + 'Tomato___Septoria_leaf_spot/'\n",
    "two_spotted_spider_mites_images_dir = image_dir + 'Tomato___Spider_mites_Two_spotted_spider_mite/'\n",
    "target_spot_images_dir = image_dir + 'Tomato___Target_Spot/'\n",
    "mosaic_virus_images_dir = image_dir + 'Tomato___Tomato_mosaic_virus/'\n",
    "yellow_leaf_curl_virus_images_dir = image_dir + 'Tomato___Tomato_Yellow_Leaf_Curl_Virus/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Tomato___healthy',\n",
       " 'Tomato___Bacterial_spot',\n",
       " 'Tomato___Target_Spot',\n",
       " '.ipynb_checkpoints',\n",
       " 'Tomato___Septoria_leaf_spot',\n",
       " 'Tomato___Late_blight',\n",
       " 'Tomato___Tomato_mosaic_virus',\n",
       " 'Tomato___Early_blight',\n",
       " 'Tomato___Tomato_Yellow_Leaf_Curl_Virus',\n",
       " 'Tomato___Spider_mites_Two_spotted_spider_mite',\n",
       " 'Tomato___Leaf_Mold']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir(\"/home/gaurav/datasets/agri/Tomato_Classifier/MultiClassData\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def loadImageFiles(dir):\n",
    "    files = [(dir + '/'+ f)\n",
    "             for f in os.listdir(dir)\n",
    "             if f.endswith('.jpg')]\n",
    "    return files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# load all the image files\n",
    "healthy_images_files = loadImageFiles(healthy_images_dir)\n",
    "bacterial_spot_images_files = loadImageFiles(bacterial_spot_images_dir)\n",
    "early_blight_images_files = loadImageFiles(early_blight_images_dir)\n",
    "late_blight_images_files = loadImageFiles(late_blight_images_dir)\n",
    "leaf_mold_images_files = loadImageFiles(leaf_mold_images_dir)\n",
    "septoria_leaf_spot_images_files = loadImageFiles(septoria_leaf_spot_images_dir)\n",
    "two_spotted_spider_mites_images_files = loadImageFiles(two_spotted_spider_mites_images_dir)\n",
    "target_spot_images_files = loadImageFiles(target_spot_images_dir)\n",
    "mosaic_virus_images_files = loadImageFiles(mosaic_virus_images_dir)\n",
    "yellow_leaf_curl_virus_images_files = loadImageFiles(yellow_leaf_curl_virus_images_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1591"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(healthy_images_files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/home/gaurav/datasets/agri/Tomato_Classifier/MultiClassData/Tomato___Bacterial_spot//55339b80-574d-4379-bc85-35558fcc0bea___UF.GRC_BS_Lab Leaf 0554_final_masked.jpg',\n",
       " '/home/gaurav/datasets/agri/Tomato_Classifier/MultiClassData/Tomato___Bacterial_spot//72932bb6-a587-4a72-8e83-867e1a295c92___GCREC_Bact.Sp 3826_final_masked.jpg']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bacterial_spot_images_files[0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def loadImages(files):\n",
    "    images = [cv2.imread(file) for file in files]\n",
    "    return images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# load all the images from name\n",
    "healthy_images = loadImages(healthy_images_files)\n",
    "bacterial_spot_images = loadImages(bacterial_spot_images_files)\n",
    "early_blight_images = loadImages(early_blight_images_files)\n",
    "late_blight_images = loadImages(late_blight_images_files)\n",
    "leaf_mold_images = loadImages(leaf_mold_images_files)\n",
    "septoria_leaf_spot_images = loadImages(septoria_leaf_spot_images_files)\n",
    "two_spotted_spider_mites_images = loadImages(two_spotted_spider_mites_images_files)\n",
    "target_spot_images = loadImages(target_spot_images_files)\n",
    "mosaic_virus_images = loadImages(mosaic_virus_images_files)\n",
    "yellow_leaf_curl_virus_images = loadImages(yellow_leaf_curl_virus_images_files)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Pre-processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Healthy Leaf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fa9e77274e0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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keYmczYVPPMblpz8JpkYvcpC+h2rpXLt2lWqliDtyCAOPOI65ubpGGIbU63WOmi0ODw+x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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa9efa6fba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow( healthy_images[4] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1591"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(healthy_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(256, 256, 3)\n"
     ]
    }
   ],
   "source": [
    "# get the image shape\n",
    "image_shape = healthy_images[0].shape\n",
    "print( image_shape )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(healthy_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1591, 256, 256, 3)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert list to numpy array\n",
    "healthy_images_np = np.array(healthy_images)\n",
    "healthy_images_np.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unhealthy Leaf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fa9e76b9668>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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e+CwbFy4QdtoEUcTVixc52H1AICT7x4c8mE+wrsN8NiXPcxwpSeMZrusyHY5othpEQUCZ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\nHTF06NDhAfz/LWONV0N01JcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa9efa6f0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow( bacterial_spot_images[4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2127"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(bacterial_spot_images)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combining healthy and unhealthy images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def numpyConverter(images):\n",
    "    return np.array(images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "healthy_images_np = numpyConverter(healthy_images)\n",
    "bacterial_spot_images_np = numpyConverter(bacterial_spot_images)\n",
    "early_blight_images_np = numpyConverter(early_blight_images)\n",
    "late_blight_images_np = numpyConverter(late_blight_images)\n",
    "leaf_mold_images_np = numpyConverter(leaf_mold_images)\n",
    "septoria_leaf_spot_images_np = numpyConverter(septoria_leaf_spot_images)\n",
    "two_spotted_spider_mites_images_np = numpyConverter(two_spotted_spider_mites_images)\n",
    "target_spot_images_np = numpyConverter(target_spot_images)\n",
    "mosaic_virus_images_np = numpyConverter(mosaic_virus_images)\n",
    "yellow_leaf_curl_virus_images_np = numpyConverter(yellow_leaf_curl_virus_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1676,)\n",
      "(256, 256, 3)\n",
      "(512, 335, 3)\n"
     ]
    }
   ],
   "source": [
    "# two_spotted_spider_mites has an image with index 1471 out of shape\n",
    "print(two_spotted_spider_mites_images_np.shape)\n",
    "print(two_spotted_spider_mites_images_np[1470].shape)\n",
    "print(two_spotted_spider_mites_images_np[1471].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# remove the 1471 index file image\n",
    "del two_spotted_spider_mites_images[1471]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "two_spotted_spider_mites_images_np = numpyConverter(two_spotted_spider_mites_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1675, 256, 256, 3)\n"
     ]
    }
   ],
   "source": [
    "print(two_spotted_spider_mites_images_np.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "all_images_np = np.concatenate( (healthy_images_np, bacterial_spot_images_np,\n",
    "                                 early_blight_images_np, late_blight_images_np, \n",
    "                                 leaf_mold_images_np, septoria_leaf_spot_images_np, \n",
    "                                 two_spotted_spider_mites_images_np, target_spot_images_np,\n",
    "                                 mosaic_virus_images_np, yellow_leaf_curl_virus_images_np), axis = 0 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18159, 256, 256, 3)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_images_np.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18159"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_images_np)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating labels array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18159"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_labels = [0] * len(healthy_images) + [1] * len(bacterial_spot_images) + \\\n",
    "           [2] * len(early_blight_images) + [3] * len(late_blight_images) + \\\n",
    "           [4] * len(leaf_mold_images) + [5] * len(septoria_leaf_spot_images) + \\\n",
    "           [6] * len(two_spotted_spider_mites_images) + [7] * len(target_spot_images) + \\\n",
    "           [8] * len(mosaic_virus_images) + [9] * len(yellow_leaf_curl_virus_images)\n",
    "        \n",
    "len(y_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 9, 9, 9])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert labels to numpy array\n",
    "y_labels_np = np.array( y_labels )\n",
    "\n",
    "y_labels_np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "/opt/anaconda3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n",
      "  return f(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "from keras.utils import np_utils\n",
    "\n",
    "# convert integers to one hot encoded\n",
    "y_one_hot_encoded = np_utils.to_categorical(y_labels_np)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "(18159, 10)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 1.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 1.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       ..., \n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  1.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  1.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  1.]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(type(y_one_hot_encoded))\n",
    "print(y_one_hot_encoded.shape)\n",
    "y_one_hot_encoded"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Splitting train and test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18159"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_images_np)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "all_indexes = list( range(len(all_images_np)) )\n",
    "sample_indexes = random.sample( all_indexes, 10000 )\n",
    "test_indexes = random.sample( sample_indexes, 1000 )\n",
    "train_indexes = list( set( sample_indexes ) - set( test_indexes ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original: Counter({9: 5357, 1: 2127, 3: 1909, 5: 1771, 6: 1675, 0: 1591, 7: 1404, 2: 1000, 4: 952, 8: 373})\n",
      "Sampled: Counter({9: 2629, 1: 1022, 3: 938, 5: 880, 6: 864, 0: 796, 7: 695, 2: 511, 4: 481, 8: 184})\n"
     ]
    }
   ],
   "source": [
    "# Checking the distribution of data\n",
    "from collections import Counter\n",
    "\n",
    "# Original dist\n",
    "print(\"Original:\",Counter(y_labels))\n",
    "# Sampled dist\n",
    "y_dist = y_labels_np[train_indexes]\n",
    "print(\"Sampled:\",Counter(y_dist))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Train images\n",
    "X_train = all_images_np[train_indexes]\n",
    "y_train = y_one_hot_encoded[train_indexes]\n",
    "\n",
    "## Test images\n",
    "X_test = all_images_np[test_indexes]\n",
    "y_test = y_one_hot_encoded[test_indexes]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sanity Check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def sanity_checker( idx ):\n",
    "    plt.imshow( X_train[idx] );\n",
    "    plt.show();\n",
    "    print( y_train[idx])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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h8mCXkGxZudOa9CvQL2YYYwipDowpsY6oE2erWlMIlLzTfsgJYwzeRRrp6J2l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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa818dff208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.  0.  0.  0.  0.  0.  0.  0.  0.  1.]\n"
     ]
    }
   ],
   "source": [
    "sanity_checker( 8400 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build the model for Classifying all tomato categories "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import keras\n",
    "import tensorflow as tf\n",
    "from keras import backend as K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# import necessary building blocks\n",
    "from keras.models import Sequential, Model\n",
    "from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Activation, Dropout, Input\n",
    "from keras.layers.advanced_activations import LeakyReLU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# normalize inputs\n",
    "X_train_norm = (X_train/255) - 0.5\n",
    "X_test_norm = (X_test/255) - 0.5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### function: train and test accuracy plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_train_val_accuracy(hist):\n",
    "    plt.plot(hist['acc'])\n",
    "    plt.plot(hist['val_acc'])\n",
    "    plt.title('model accuracy')\n",
    "    plt.ylabel('accuracy')\n",
    "    plt.xlabel('epoch')\n",
    "    plt.legend(['train', 'test'], loc='upper left')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### function: train and test loss plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_train_val_loss(hist):\n",
    "    plt.plot(hist['loss'])\n",
    "    plt.plot(hist['val_loss'])\n",
    "    plt.title('model loss')\n",
    "    plt.ylabel('loss')\n",
    "    plt.xlabel('epoch')\n",
    "    plt.legend(['train', 'test'], loc='upper left')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model: Use pre-built networks via transfer learning\n",
    "\n",
    "** Architecture **\n",
    "- Conv -> Maxpool -> Conv-> Maxpool -> Maxpool -> Conv -> Maxpool -> Softmax\n",
    "\n",
    "** Optimizer **\n",
    "\n",
    "- Adam\n",
    "- Batch size = 256\n",
    "- Epoch = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "K.clear_session()  # clear default graph\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Conv2D(filters=16, kernel_size=(3,3), strides=1, padding='same', input_shape=image_shape))\n",
    "model.add(LeakyReLU(0.1))\n",
    "                            \n",
    "model.add(MaxPooling2D(pool_size=(3, 3)))\n",
    "\n",
    "model.add(Conv2D(filters=32, kernel_size=(3,3), strides=1, padding='same', input_shape=image_shape))\n",
    "model.add(LeakyReLU(0.1))\n",
    "                            \n",
    "model.add(MaxPooling2D(pool_size=(3, 3)))\n",
    "\n",
    "model.add(Conv2D(filters=64, kernel_size=(3,3), strides=1, padding='same', input_shape=image_shape))\n",
    "model.add(LeakyReLU(0.1))\n",
    "                            \n",
    "model.add(MaxPooling2D(pool_size=(3, 3)))\n",
    "\n",
    "model.add(Flatten())\n",
    "    \n",
    "model.add(Dense(256))\n",
    "model.add(LeakyReLU(0.1))\n",
    "#model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Dense(64))\n",
    "model.add(LeakyReLU(0.1))\n",
    "#model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Dense(10))\n",
    "model.add(Activation('softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 256, 256, 16)      448       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_1 (LeakyReLU)    (None, 256, 256, 16)      0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 85, 85, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 85, 85, 32)        4640      \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_2 (LeakyReLU)    (None, 85, 85, 32)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 28, 28, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 28, 28, 64)        18496     \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_3 (LeakyReLU)    (None, 28, 28, 64)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 9, 9, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 5184)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 256)               1327360   \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_4 (LeakyReLU)    (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 64)                16448     \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_5 (LeakyReLU)    (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 10)                650       \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 10)                0         \n",
      "=================================================================\n",
      "Total params: 1,368,042\n",
      "Trainable params: 1,368,042\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 9000 samples, validate on 1000 samples\n",
      "Epoch 1/20\n",
      "9000/9000 [==============================] - 172s 19ms/step - loss: 1.3753 - acc: 0.5430 - val_loss: 0.9282 - val_acc: 0.6970\n",
      "Epoch 2/20\n",
      "9000/9000 [==============================] - 173s 19ms/step - loss: 0.7258 - acc: 0.7561 - val_loss: 0.5989 - val_acc: 0.8060\n",
      "Epoch 3/20\n",
      "9000/9000 [==============================] - 173s 19ms/step - loss: 0.5410 - acc: 0.8160 - val_loss: 0.5161 - val_acc: 0.8160\n",
      "Epoch 4/20\n",
      "9000/9000 [==============================] - 173s 19ms/step - loss: 0.3988 - acc: 0.8649 - val_loss: 0.4938 - val_acc: 0.8230\n",
      "Epoch 5/20\n",
      "9000/9000 [==============================] - 173s 19ms/step - loss: 0.3203 - acc: 0.8929 - val_loss: 0.4190 - val_acc: 0.8600\n",
      "Epoch 6/20\n",
      "9000/9000 [==============================] - 173s 19ms/step - loss: 0.2557 - acc: 0.9110 - val_loss: 0.4127 - val_acc: 0.8570\n",
      "Epoch 7/20\n",
      "9000/9000 [==============================] - 173s 19ms/step - loss: 0.1981 - acc: 0.9324 - val_loss: 0.2939 - val_acc: 0.9020\n",
      "Epoch 8/20\n",
      "9000/9000 [==============================] - 173s 19ms/step - loss: 0.1430 - acc: 0.9522 - val_loss: 0.5074 - val_acc: 0.8560\n",
      "Epoch 9/20\n",
      "9000/9000 [==============================] - 173s 19ms/step - loss: 0.1270 - acc: 0.9580 - val_loss: 0.3575 - val_acc: 0.8960\n",
      "Epoch 10/20\n",
      "9000/9000 [==============================] - 173s 19ms/step - loss: 0.1403 - acc: 0.9503 - val_loss: 0.3424 - val_acc: 0.9050\n",
      "Epoch 11/20\n",
      "9000/9000 [==============================] - 173s 19ms/step - loss: 0.0772 - acc: 0.9763 - val_loss: 0.3090 - val_acc: 0.9170\n",
      "Epoch 12/20\n",
      "9000/9000 [==============================] - 173s 19ms/step - loss: 0.0485 - acc: 0.9867 - val_loss: 0.3336 - val_acc: 0.9100\n",
      "Epoch 13/20\n",
      "9000/9000 [==============================] - 172s 19ms/step - loss: 0.0282 - acc: 0.9929 - val_loss: 0.3108 - val_acc: 0.9220\n",
      "Epoch 14/20\n",
      "9000/9000 [==============================] - 172s 19ms/step - loss: 0.0332 - acc: 0.9898 - val_loss: 0.3633 - val_acc: 0.9030\n",
      "Epoch 15/20\n",
      "9000/9000 [==============================] - 172s 19ms/step - loss: 0.0273 - acc: 0.9931 - val_loss: 0.3564 - val_acc: 0.9130\n",
      "Epoch 16/20\n",
      "9000/9000 [==============================] - 173s 19ms/step - loss: 0.0375 - acc: 0.9896 - val_loss: 0.4104 - val_acc: 0.9050\n",
      "Epoch 17/20\n",
      "9000/9000 [==============================] - 173s 19ms/step - loss: 0.0621 - acc: 0.9814 - val_loss: 0.3067 - val_acc: 0.9210\n",
      "Epoch 18/20\n",
      "9000/9000 [==============================] - 173s 19ms/step - loss: 0.0098 - acc: 0.9986 - val_loss: 0.3107 - val_acc: 0.9270\n",
      "Epoch 19/20\n",
      "9000/9000 [==============================] - 173s 19ms/step - loss: 0.0028 - acc: 1.0000 - val_loss: 0.3429 - val_acc: 0.9220\n",
      "Epoch 20/20\n",
      "9000/9000 [==============================] - 172s 19ms/step - loss: 0.0013 - acc: 1.0000 - val_loss: 0.3580 - val_acc: 0.9240\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer='adam',\n",
    "              loss = 'categorical_crossentropy',\n",
    "              metrics = ['accuracy'])\n",
    "\n",
    "BATCH_SIZE = 128\n",
    "EPOCHS = 20\n",
    "\n",
    "history = model.fit(\n",
    "    X_train_norm, \n",
    "    y_train,  # prepared data\n",
    "    batch_size=BATCH_SIZE,\n",
    "    epochs=EPOCHS,\n",
    "    validation_data=(X_test_norm, y_test),\n",
    "    verbose=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AV1nNTS+u5LWVOzhjeBZ//fpouieFcBHxujp46VooWgtXvAAZxxze/id8D3Ys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\nFIwxxjT4/xvdscUab7MHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa818ee0f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_train_val_accuracy(history.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Q8/5KkuIieOqyYVz2/Pf89r8rK0Y/10v5NaR7nW6rkQ7tg+JceyvYZ7uzFufZ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\nKmhSUEopVUGTglJKqQr/D1SyttLjAAVBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa3f9b36588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_train_val_loss(history.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.6970000023841858, 0.80600000143051143, 0.81600000143051143, 0.82299999809265134, 0.86000000095367435, 0.85700000047683711, 0.90200000000000002, 0.85599999809265137, 0.89600000095367427, 0.90499999856948854, 0.91699999761581419, 0.90999999761581418, 0.92200000000000004, 0.90299999952316279, 0.91300000238418577, 0.90500000238418576, 0.92099999856948855, 0.9269999976158142, 0.92199999761581419, 0.92399999761581419]\n"
     ]
    }
   ],
   "source": [
    "print(history.history['val_acc'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model 2: Handling overfitting as Model 1 + dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "K.clear_session()  # clear default graph\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Conv2D(filters=16, kernel_size=(3,3), strides=1, padding='same', input_shape=image_shape))\n",
    "model.add(LeakyReLU(0.1))\n",
    "                            \n",
    "model.add(MaxPooling2D(pool_size=(3, 3)))\n",
    "\n",
    "model.add(Conv2D(filters=32, kernel_size=(3,3), strides=1, padding='same', input_shape=image_shape))\n",
    "model.add(LeakyReLU(0.1))\n",
    "                            \n",
    "model.add(MaxPooling2D(pool_size=(3, 3)))\n",
    "\n",
    "model.add(Conv2D(filters=64, kernel_size=(3,3), strides=1, padding='same', input_shape=image_shape))\n",
    "model.add(LeakyReLU(0.1))\n",
    "                            \n",
    "model.add(MaxPooling2D(pool_size=(3, 3)))\n",
    "\n",
    "model.add(Flatten())\n",
    "    \n",
    "model.add(Dense(256))\n",
    "model.add(LeakyReLU(0.1))\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Dense(64))\n",
    "model.add(LeakyReLU(0.1))\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Dense(10))\n",
    "model.add(Activation('softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 256, 256, 16)      448       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_1 (LeakyReLU)    (None, 256, 256, 16)      0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 85, 85, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 85, 85, 32)        4640      \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_2 (LeakyReLU)    (None, 85, 85, 32)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 28, 28, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 28, 28, 64)        18496     \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_3 (LeakyReLU)    (None, 28, 28, 64)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 9, 9, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 5184)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 256)               1327360   \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_4 (LeakyReLU)    (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 64)                16448     \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_5 (LeakyReLU)    (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 10)                650       \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 10)                0         \n",
      "=================================================================\n",
      "Total params: 1,368,042\n",
      "Trainable params: 1,368,042\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 9000 samples, validate on 1000 samples\n",
      "Epoch 1/30\n",
      "9000/9000 [==============================] - 172s 19ms/step - loss: 1.7059 - acc: 0.4203 - val_loss: 1.0491 - val_acc: 0.6670\n",
      "Epoch 2/30\n",
      "9000/9000 [==============================] - 172s 19ms/step - loss: 1.1029 - acc: 0.6310 - val_loss: 0.7404 - val_acc: 0.7500\n",
      "Epoch 3/30\n",
      "9000/9000 [==============================] - 172s 19ms/step - loss: 0.8271 - acc: 0.7334 - val_loss: 0.5641 - val_acc: 0.8190\n",
      "Epoch 4/30\n",
      "9000/9000 [==============================] - 172s 19ms/step - loss: 0.6782 - acc: 0.7756 - val_loss: 0.4881 - val_acc: 0.8320\n",
      "Epoch 5/30\n",
      "9000/9000 [==============================] - 172s 19ms/step - loss: 0.5606 - acc: 0.8181 - val_loss: 0.4737 - val_acc: 0.8580\n",
      "Epoch 6/30\n",
      "9000/9000 [==============================] - 172s 19ms/step - loss: 0.4852 - acc: 0.8429 - val_loss: 0.3463 - val_acc: 0.8910\n",
      "Epoch 7/30\n",
      "9000/9000 [==============================] - 172s 19ms/step - loss: 0.4159 - acc: 0.8612 - val_loss: 0.3607 - val_acc: 0.8850\n",
      "Epoch 8/30\n",
      "9000/9000 [==============================] - 172s 19ms/step - loss: 0.3566 - acc: 0.8808 - val_loss: 0.3334 - val_acc: 0.8960\n",
      "Epoch 9/30\n",
      "9000/9000 [==============================] - 172s 19ms/step - loss: 0.3292 - acc: 0.8889 - val_loss: 0.3037 - val_acc: 0.8970\n",
      "Epoch 10/30\n",
      "9000/9000 [==============================] - 172s 19ms/step - loss: 0.2977 - acc: 0.9054 - val_loss: 0.3281 - val_acc: 0.9050\n",
      "Epoch 11/30\n",
      "9000/9000 [==============================] - 172s 19ms/step - loss: 0.2401 - acc: 0.9214 - val_loss: 0.2549 - val_acc: 0.9290\n",
      "Epoch 12/30\n",
      "9000/9000 [==============================] - 172s 19ms/step - loss: 0.2232 - acc: 0.9268 - val_loss: 0.2833 - val_acc: 0.9140\n",
      "Epoch 13/30\n",
      "9000/9000 [==============================] - 173s 19ms/step - loss: 0.1964 - acc: 0.9346 - val_loss: 0.2375 - val_acc: 0.9240\n",
      "Epoch 14/30\n",
      "9000/9000 [==============================] - 172s 19ms/step - loss: 0.1705 - acc: 0.9431 - val_loss: 0.2559 - val_acc: 0.9130\n",
      "Epoch 15/30\n",
      "9000/9000 [==============================] - 172s 19ms/step - loss: 0.1758 - acc: 0.9451 - val_loss: 0.2463 - val_acc: 0.9210\n",
      "Epoch 16/30\n",
      "9000/9000 [==============================] - 172s 19ms/step - loss: 0.1599 - acc: 0.9499 - val_loss: 0.3294 - val_acc: 0.9060\n",
      "Epoch 17/30\n",
      "9000/9000 [==============================] - 172s 19ms/step - loss: 0.1484 - acc: 0.9533 - val_loss: 0.2479 - val_acc: 0.9300\n",
      "Epoch 18/30\n",
      "9000/9000 [==============================] - 172s 19ms/step - loss: 0.1185 - acc: 0.9610 - val_loss: 0.2722 - val_acc: 0.9180\n",
      "Epoch 19/30\n",
      "9000/9000 [==============================] - 172s 19ms/step - loss: 0.0944 - acc: 0.9691 - val_loss: 0.2710 - val_acc: 0.9310\n",
      "Epoch 20/30\n",
      "9000/9000 [==============================] - 172s 19ms/step - loss: 0.0978 - acc: 0.9684 - val_loss: 0.2478 - val_acc: 0.9350\n",
      "Epoch 21/30\n",
      "9000/9000 [==============================] - 173s 19ms/step - loss: 0.0965 - acc: 0.9682 - val_loss: 0.2689 - val_acc: 0.9310\n",
      "Epoch 22/30\n",
      "9000/9000 [==============================] - 172s 19ms/step - loss: 0.0810 - acc: 0.9744 - val_loss: 0.2744 - val_acc: 0.9320\n",
      "Epoch 23/30\n",
      "9000/9000 [==============================] - 172s 19ms/step - loss: 0.0664 - acc: 0.9781 - val_loss: 0.2640 - val_acc: 0.9310\n",
      "Epoch 24/30\n",
      "9000/9000 [==============================] - 172s 19ms/step - loss: 0.0710 - acc: 0.9779 - val_loss: 0.2599 - val_acc: 0.9300\n",
      "Epoch 25/30\n",
      "9000/9000 [==============================] - 172s 19ms/step - loss: 0.0747 - acc: 0.9758 - val_loss: 0.2782 - val_acc: 0.9260\n",
      "Epoch 26/30\n",
      "9000/9000 [==============================] - 172s 19ms/step - loss: 0.0579 - acc: 0.9814 - val_loss: 0.2721 - val_acc: 0.9380\n",
      "Epoch 27/30\n",
      "9000/9000 [==============================] - 172s 19ms/step - loss: 0.0489 - acc: 0.9830 - val_loss: 0.2648 - val_acc: 0.9270\n",
      "Epoch 28/30\n",
      "9000/9000 [==============================] - 172s 19ms/step - loss: 0.0790 - acc: 0.9764 - val_loss: 0.2605 - val_acc: 0.9380\n",
      "Epoch 29/30\n",
      "9000/9000 [==============================] - 172s 19ms/step - loss: 0.0655 - acc: 0.9806 - val_loss: 0.3792 - val_acc: 0.9140\n",
      "Epoch 30/30\n",
      "9000/9000 [==============================] - 172s 19ms/step - loss: 0.0557 - acc: 0.9826 - val_loss: 0.2702 - val_acc: 0.9280\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer='adam',\n",
    "              loss = 'categorical_crossentropy',\n",
    "              metrics = ['accuracy'])\n",
    "\n",
    "BATCH_SIZE = 128\n",
    "EPOCHS = 30\n",
    "\n",
    "history = model.fit(\n",
    "    X_train_norm, \n",
    "    y_train,  # prepared data\n",
    "    batch_size=BATCH_SIZE,\n",
    "    epochs=EPOCHS,\n",
    "    validation_data=(X_test_norm, y_test),\n",
    "    verbose=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa3f9b21da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_train_val_accuracy(history.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plot_train_val_loss(history.history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model: Use pre-built networks via transfer learning\n",
    "\n",
    "** Architecture **\n",
    "- VGG16\n",
    "\n",
    "** Optimizer **\n",
    "\n",
    "- Adam\n",
    "- Batch size = 256\n",
    "- Epoch = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# from keras.applications import VGG16\n",
    "\n",
    "# # Loading the pre-trained weights\n",
    "# K.clear_session()\n",
    "\n",
    "# conv_model = VGG16(weights='imagenet',\n",
    "#                   include_top=False, # Download only the conv network while skip the last two fully connected layers\n",
    "#                   input_shape=image_shape)\n",
    "\n",
    "# # Model summary\n",
    "# conv_model.summary()\n",
    "# # Freeze the layers \n",
    "# for layer in conv_model.layers:\n",
    "#     layer.trainable = False\n",
    "    \n",
    "# # Add the custom layers to the top of the network\n",
    "# x = conv_model.output # this has a shape of (None, 8, 8, 512)\n",
    "\n",
    "# x = Flatten()(x) # flatten the output from the conv net of vgg16\n",
    "# x = Dense(128, activation = \"relu\")(x)\n",
    "# #x = Dropout(0.5)(x)\n",
    "# x = Dense(64, activation = \"relu\")(x)\n",
    "# #x = Dropout(0.5)(x)\n",
    "# outputs = Dense(10, activation = 'softmax')(x)\n",
    "\n",
    "# vgg16_model = Model(input = conv_model.input, output = outputs)\n",
    "\n",
    "# # Final model summary\n",
    "# vgg16_model.summary()\n",
    "\n",
    "# vgg16_model.compile(optimizer = 'adam',\n",
    "#               loss = 'categorical_crossentropy',\n",
    "#               metrics = ['accuracy'])\n",
    "\n",
    "# BATCH_SIZE = 256\n",
    "# EPOCHS = 10\n",
    "\n",
    "# history = vgg16_model.fit(\n",
    "#     X_train_norm, \n",
    "#     y_train,  # prepared data\n",
    "#     batch_size=BATCH_SIZE,\n",
    "#     epochs=EPOCHS,\n",
    "#     validation_data=(X_test_norm, y_test),\n",
    "#     verbose=1\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
